This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.展开更多
Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has bec...Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.展开更多
Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of th...Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.展开更多
基金supported by the National Key Research and Development Program of China(2016YFD0700605)the Fundamental Research Funds for the Central Universities(JZ2016HGBZ1035)the Anhui University Natural Science Research Project(KJ2017A891)
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
基金This work was supported by the National Natural Science Foundation of China(Nos.61973120,62076095,61673175,and 61573144).
文摘Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.
基金supported by National Social Science Foundation of China under the project of 18BGL003.
文摘Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.